Derivatives of scattering profiles: tools for nanoparticle characterization
Bibliographic record
Abstract
This paper presents a new approach to characterize nanoparticles using derivatives of scattering profiles of evanescent waves/surface plasmons. We start the procedure using the scattering profiles for an unknown configuration of nanoparticles, either from physical experiments or numerical simulations conducted for different nanoparticles on surfaces. We apply the statistical technique of compound estimation to recover the derivatives of scattering profiles. The L(1) discrepancies with the corresponding curves from known configurations are used to identify the most plausible configuration of particles that could yield the "experimental" profiles. We conduct a simulation study to see how often the new procedure correctly recovers the agglomeration level for gold spherical nanoparticles on a thin gold film. The results suggest that first derivatives are much more effective for characterization than undifferentiated profiles and that M(33) is the most useful element for distinguishing among configurations. The proposed compound estimation technique is more effective than typical inverse analyses based on look-up tables and can be used effectively in nanoparticle characterization platforms.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".